Data lakes and the promise of unsiloed data

Data lakes that can scale at the pace of the cloud remove integration barriers and clear a path for more timely and informed business decisions.

Data lakes: An emerging approach to cloud-based big data

UC Irvine Medical Center maintains millions of records for more than a million patients, including radiology images and other semi-structured reports, unstructured physicians’ notes, plus volumes of spreadsheet data. To solve the challenge the hospital faced with data storage, integration, and accessibility, the hospital created a data lake based on a Hadoop architecture, which enables distributed big data processing by using broadly accepted open software standards and massively parallel commodity hardware.

A basic Hadoop architecture for scalable data lake infrastructure

Enterprises across industries are starting to extract and place data for analytics into a single, Hadoop-based repository.

Like the hospital, enterprises across industries are starting to extract and place data for analytics into a single Hadoop-based repository without first transforming the data the way they would need to for a relational data warehouse. The basic concepts behind Hadoop were devised by Google to meet its need for a flexible, cost-effective data processing model that could scale as data volumes grew faster than ever. Yahoo, Facebook, Netflix, and others whose business models also are based on managing enormous data volumes quickly adopted similar methods. Costs were certainly a factor, as Hadoop can be 10 to 100 times less expensive to deploy than conventional data warehousing. Another driver of adoption has been the opportunity to defer labor-intensive schema development and data cleanup until an organization has identified a clear business need. And data lakes are more suitable for the less-structured data these companies needed to process.

Today, companies in all industries find themselves at a similar point of necessity. Enterprises that must use enormous volumes and myriad varieties of data to respond to regulatory and competitive pressures are adopting data lakes. Data lakes are an emerging and powerful approach to the challenges of data integration as enterprises increase their exposure to mobile and cloud-based applications, the sensor-driven Internet of Things, and other aspects of what PwC calls the New IT Platform.

Hadoop can be 10 to 100 times less expensive to deploy than conventional data warehousing.

What is a data lake?

Why a data lake?

Data lakes can help resolve the nagging problem of accessibility and data integration. Using big data infrastructures, enterprises are starting to pull together increasing data volumes for analytics or simply to store for undetermined future use. (See the sidebar “Data lakes defined.”) Mike Lang, CEO of Revelytix, a provider of data management tools for Hadoop, notes that “Business owners at the C level are saying, ‘Hey guys, look. It’s no longer inordinately expensive for us to store all of our data. I want all of you to make copies. OK, your systems are busy. Find the time, get an extract, and dump it in Hadoop.’”

Previous approaches to broad-based data integration have forced all users into a common predetermined schema, or data model. Unlike this monolithic view of a single enterprise-wide data model, the data lake relaxes standardization and defers modeling, resulting in a nearly unlimited potential for operational insight and data discovery. As data volumes, data variety, and metadata richness grow, so does the benefit.

Recent innovation is helping companies to collaboratively create models—or views—of the data and then manage incremental improvements to the metadata. Data scientists and business analysts using the newest lineage tracking tools such as Revelytix Loom or Apache Falcon can follow each other’s purpose-built data schemas. The lineage tracking metadata also is placed in the Hadoop Distributed File System (HDFS)—which stores pieces of files across a distributed cluster of servers in the cloud—where the metadata is accessible and can be collaboratively refined. Analytics drawn from the lake become increasingly valuable as the metadata describing different views of the data accumulates.

Every industry has a potential data lake use case. A data lake can be a way to gain more visibility or put an end to data silos. Many companies see data lakes as an opportunity to capture a 360-degree view of their customers or to analyze social media trends.

In the financial services industry, where Dodd-Frank regulation is one impetus, an institution has begun centralizing multiple data warehouses into a repository comparable to a data lake, but one that standardizes on XML. The institution is moving reconciliation, settlement, and Dodd-Frank reporting to the new platform. In this case, the approach reduces integration overhead because data is communicated and stored in a standard yet flexible format suitable for less-structured data. The system also provides a consistent view of a customer across operational functions, business functions, and products.

Motivating factors behind the move to data lakes

Relational data warehouses and their big price tags have long dominated complex analytics, reporting, and operations. (The hospital described earlier, for example, first tried a relational data warehouse.) However, their slow-changing data models and rigid field-to-field integration mappings are too brittle to support big data volume and variety. The vast majority of these systems also leave business users dependent on IT for even the smallest enhancements, due mostly to inelastic design, unmanageable system complexity, and low system tolerance for human error. The data lake approach circumvents these problems.

Freedom from the shackles of one big data model

Job number one in a data lake project is to pull all data together into one repository while giving minimal attention to creating schemas that define integration points between disparate data sets. This approach facilitates access, but the work required to turn that data into actionable insights is a substantial challenge. While integrating the data takes place at the Hadoop layer, contextualizing the metadata takes place at schema creation time.

Integrating data involves fewer steps because data lakes don’t enforce a rigid metadata schema as do relational data warehouses. Instead, data lakes support a concept known as late binding, or schema on read, in which users build custom schema into their queries. Data is bound to a dynamic schema created upon query execution. The late-binding principle shifts the data modeling from centralized data warehousing teams and database administrators, who are often remote from data sources, to localized teams of business analysts and data scientists, who can help create flexible, domain-specific context. For those accustomed to SQL, this shift opens a whole new world.

In this approach, the more that is known about the metadata, the easier it is to query. Pre-tagged data, such as Extensible Markup Language (XML), JavaScript Object Notation (JSON), or Resource Description Framework (RDF), offers a starting point and is highly useful in implementations with limited data variety. In most cases, however, pre-tagged data is a small portion of incoming data formats.

Early lessons and pitfalls to avoid

Some data lake initiatives have not succeeded, producing instead more silos or empty sandboxes. Given the risk, everyone is proceeding cautiously. “We see customers creating big data graveyards, dumping everything into HDFS and hoping to do something with it down the road. But then they just lose track of what’s there,” says Sean Martin, CTO of Cambridge Semantics, a data management tools provider.

Companies avoid creating big data graveyards by developing and executing a solid strategic plan that applies the right technology and methods to the problem. Few technologies in recent memory have as much change potential as Hadoop and the NoSQL (Not only SQL) category of databases, especially when they can enable a single enterprise-wide repository and provide access to data previously trapped in silos. The main challenge is not creating a data lake, but taking advantage of the opportunities it presents. A means of creating, enriching, and managing semantic metadata incrementally is essential.

“We see customers creating big data graveyards, dumping everything into HDFS and hoping to do something with it down the road. But then they just lose track of what’s there.”

—Sean Martin, Cambridge Semantics

Data flow in the data lake

How a data lake matures

Sourcing new data into the lake can occur gradually and will not impact existing models. The lake starts with raw data, and it matures as more data flows in, as users and machines build up metadata, and as user adoption broadens. Ambiguous and competing terms eventually converge into a shared understanding (that is, semantics) within and across business domains. Data maturity results as a natural outgrowth of the ongoing user interaction and feedback at the metadata management layer—interaction that continually refines the lake and enhances discovery. (See the sidebar “Maturity and governance.”)

With the data lake, users can take what is relevant and leave the rest. Individual business domains can mature independently and gradually. Perfect data classification is not required. Users throughout the enterprise can see across all disciplines, not limited by organizational silos or rigid schema.